fixing build issue

.travis.yml

@@ -7,10 +7,8 @@ python:
 install:
   - pip install --upgrade pip
   - pip install -U pytest
-  - pip install -U pylint
   - pip install .[tests,optional]
 
 script:
   - cd tests; pytest --disable-warnings --cov=dvt --cov-report term-missing .
   - cd tests; codecov
-  - pylint dvt

dvt/aggregate/core.py

@@ -34,4 +34,4 @@ def aggregate(self, ldframe, **kwargs):
             While not strictly enforced, subclasses should return a DictFrame
             or dictionary of DictFrames from the aggregate method.
         """
-        return # pragma nocov
+        return # pragma: no cover

dvt/aggregate/people.py

@@ -134,7 +134,7 @@ def make_fprint_from_images(dinput):
 
     fpobj.process(ImageInput(input_paths=join(dinput, "", "*")))
 
-    faces = fpobj.collect('face')
+    ss = fpobj.collect('face')
     names = [splitext(basename(x))[0] for x in faces['frame']]
 
     return faces['embed'], names

dvt/annotate/core.py

@@ -227,7 +227,7 @@ def annotate(self, batch):
             (or first shape value, in the case of numpy array). Can also return
             None, in which case nothing is added to the current output.
         """
-        return   # pragma nocov
+        return   # pragma: no cover
 
 
 class FrameInput:

dvt/cli.py

@@ -32,6 +32,7 @@ def run_cli():
             doutput=args.dirout,
             diff_co=args.diff_cutoff,
             cut_min_length=args.cut_min_length,
+            path_to_faces=args.path_to_faces
         )
         vpipe.make_breaks(freq=args.frequency)
         vpipe.run(level=args.pipeline_level)
@@ -95,6 +96,13 @@ def _get_arg_parse():
         action="store_true",
         help="flag to indicate that the pipeline should run quietly",
     )
+    parser.add_argument(
+        "--path-to-faces",
+        type=str,
+        default=None,
+        help="Path to directory containing protype faces (optional). See "
+        "tutorial on the commandline interface for more details.",
+    )
 
     return parser
 

dvt/pipeline/video.py

@@ -28,12 +28,13 @@
 
 from ..annotate.cielab import CIElabAnnotator
 from ..annotate.core import FrameProcessor, FrameInput
-from ..annotate.face import FaceAnnotator, FaceDetectMtcnn
+from ..annotate.face import FaceAnnotator, FaceDetectMtcnn, FaceEmbedVgg2
 from ..annotate.meta import MetaAnnotator
 from ..annotate.obj import ObjectAnnotator, ObjectDetectRetinaNet
 from ..annotate.opticalflow import OpticalFlowAnnotator
 from ..annotate.png import PngAnnotator
 from ..aggregate.display import DisplayAggregator
+from ..aggregate.people import PeopleAggregator, make_fprint_from_images
 from ..aggregate.length import ShotLengthAggregator
 from ..utils import setup_tensorflow, _format_time, DictFrame
 from .utils import _get_cuts, _get_meta
@@ -52,9 +53,20 @@ class VideoPipeline:
             values produce fewer cuts.
         cut_min_length (int): minimum length of a detected cut in frames;
             higher values produce few cuts.
+        path_to_faces (str): Path to directory containing protype faces
+            (optional). See tutorial on the commandline interface for more
+            details.
     """
 
-    def __init__(self, finput, doutput=None, diff_co=10, cut_min_length=30):
+    def __init__(
+        self,
+        finput,
+        doutput=None,
+        diff_co=10,
+        cut_min_length=30,
+        path_to_faces=None
+    ):
+
         setup_tensorflow()
 
         # find absolute path to the input and determine the output location
@@ -69,6 +81,8 @@ def __init__(self, finput, doutput=None, diff_co=10, cut_min_length=30):
         self.doutput = os.path.join(doutput, fname)
         self.diff_co = diff_co
         self.cut_min_length = cut_min_length
+        self.path_to_faces = path_to_faces
+
         self.cuts = None
         self.pipeline_data = None
 
@@ -145,6 +159,20 @@ def _run_pipeline(self):
             ObjectAnnotator(detector=ObjectDetectRetinaNet(), frames=frames)
         )
 
+        if self.path_to_faces is not None:
+            fembed, fnames = make_fprint_from_images(self.path_to_faces)
+            fpobj.load_annotator(
+                FaceAnnotator(
+                    detector=FaceDetectMtcnn(),
+                    embedding=FaceEmbedVgg2()
+                    frames=frames
+                )
+            )
+        else:
+            fpobj.load_annotator(
+                FaceAnnotator(detector=FaceDetectMtcnn(), frames=frames)
+            )
+
         fri = FrameInput(self.finput, bsize=128)
         fpobj.process(fri)
         self.pipeline_data = fpobj.collect_all()
@@ -169,6 +197,9 @@ def _make_json(self):
         fps = self.pipeline_data["meta"]["fps"][0]
         ldata = self.pipeline_data["length"]
 
+        if self.path_to_faces is not None:
+            people = PeopleAggregator(self.pipeline_data)
+
         output = []
         for fnum in range(nframes):